[source]

IRAPSCore

galaxy_ml.binarize_target._iraps_classifier.IRAPSCore(n_iter=1000, positive_thres=-1, negative_thres=0, verbose=0, n_jobs=1, pre_dispatch='2*n_jobs', random_state=None, parallel_backend=None)

Base class of IRAPSClassifier From sklearn BaseEstimator: get_params() set_params()

Parameters

  • n_iter: int
    sample count
  • positive_thres: float
    z_score shreshold to discretize positive target values
  • negative_thres: float
    z_score threshold to discretize negative target values
  • verbose: int
    0 or geater, if not 0, print progress
  • n_jobs: int, default=1
    The number of CPUs to use to do the computation.
  • pre_dispatch: int, or string.
    Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs'
  • random_state: int or None
  • parallel_backend: str, default is None.
    Used in joblib.Parallel. Common options are 'loky', 'multiprocessing', and 'threading'. If None, loky is used.

[source]

IRAPSClassifier

galaxy_ml.binarize_target._iraps_classifier.IRAPSClassifier(iraps_core, p_thres=0.0001, fc_thres=0.1, occurrence=0.8, discretize=-1, memory=None, min_signature_features=1)

Extend the bases of both sklearn feature_selector and classifier. From sklearn BaseEstimator: get_params() set_params() From sklearn _BaseFilter: get_support() fit_transform(X) transform(X) From sklearn RegressorMixin: score(X, y): R2 New: predict(X) predict_label(X) get_signature() Properties: discretize_value

Parameters

  • iraps_core: object
  • p_thres: float, threshold for p_values
  • fc_thres: float, threshold for fold change or mean difference
  • occurrence: float, occurrence rate selected by set of p_thres and fc_thres
  • discretize: float, threshold of z_score to discretize target value
  • memory: None, str or joblib.Memory object
  • min_signature_features: int, the mininum number of features in a signature